This is documentation for Mathematica 8, which was
based on an earlier version of the Wolfram Language.
View current documentation (Version 11.2)
Design Using State-Space Models
Mathematica provides powerful functions to compute state-feedback and estimator gains using pole-placement or optimal techniques. In addition, it has functions that directly assemble regulator and estimator models based on design specifications or precomputed gains.
Pole Placement
StateFeedbackGains feedback gains computed using pole placement
EstimatorGains estimator gains computed using pole placement
Optimal Control and Estimation
LQRegulatorGains feedback gains that minimize a quadratic cost function
LQOutputRegulatorGains feedback gains that minimize a quadratic output cost function
LQEstimatorGains estimator gains that minimize a quadratic cost function
    
DiscreteLQRegulatorGains emulated discrete-time feedback gains
DiscreteLQEstimatorGains emulated discrete-time estimator gains
Controllers and Estimators
KalmanEstimator Kalman estimator with specified covariance matrices
LQGRegulator linear quadratic Gaussian (LQG) regulator
StateOutputEstimator estimator model with specified gains
EstimatorRegulator regulator model with specified estimator and feedback gains
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